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Checklist · Ai Infrastructure

Ai Infrastructure Launch Checklist for 2026

Use this launch checklist to guide your AI infrastructure build in 2026. Tasks are organized into three phases so you always know what to prioritize next. Check off items as you move from foundation through execution to launch [free tools](/tools).

9 checklist items Updated from migrated LaunchTry SEO content

Phase 01

Foundation

3 tasks
  • c1
    medium1 week

    Define goals and KPIs (Ai Infrastructure)

    Align the team on success metrics: latency targets, cost per inference, throughput and accuracy thresholds for your AI infrastructure deployment.

  • c2
    critical1 day

    Identify target audience (Ai Infrastructure)

    Map end-users and use cases—MLOps teams, data engineers, or production ML applications that will depend on your infrastructure choices.

  • c3
    medium1 week

    Audit current state (Ai Infrastructure)

    Inventory existing GPU/TPU capacity, inference libraries (TensorFlow, PyTorch), deployment targets (edge, cloud, on-prem) and any technical debt from prior systems.

Phase 02

Execution

3 tasks
  • c4
    medium1 week

    Prioritize high-impact tasks (Ai Infrastructure)

    Rank infrastructure decisions by ROI: model serving vs. fine-tuning vs. RAG, batching strategies, caching layers and cost optimization.

  • c5
    medium1 week

    Assign owners and deadlines (Ai Infrastructure)

    Assign ownership for each infrastructure component—model deployment, monitoring, scaling, cost tracking—with clear deadlines and rollback plans.

  • c6
    medium1 week

    Set up tracking (Ai Infrastructure)

    Set up observability: inference latency dashboards, GPU utilization, cost per request and error rate dashboards to track health in real time.

Phase 03

Launch & Review

3 tasks
  • c7
    critical1 day

    Ship and verify (Ai Infrastructure)

    Deploy to production with staged rollout—shadow traffic, canary deployments or A/B testing to catch regressions before full traffic shift.

  • c8
    high2-3 days

    Measure against KPIs (Ai Infrastructure)

    Measure inference latency, throughput, cost per request and model accuracy against your KPIs from phase 1; iterate on bottlenecks.

  • c9
    critical1 day

    Iterate on results (Ai Infrastructure)

    Act on early feedback: optimize batch sizes, add caching, refine quantization, or shift workloads to cheaper inference endpoints.

Pro tips

  • Tackle critical items first
  • Review the checklist weekly
  • Adapt phases to your ai infrastructure context